We also see that our grammar-based structural CNN decoder achieves similar results to the state-of-the-art neural models [1]}, [2]}.
It should also be pointed out that in semantic parsing, we do not achieve a large performance boost as in HearthStone (HS) code generation. This is probably because the logic form for semantic parsing is usually short, containing only 1/4–1/3 tokens as in HS, and thus, both RNN and CNN are fine for logic form generation. This experiment nevertheless provides additional evidence of the generalizability and flexibility of our CNN code generation, since our model is basically designed for long programs (such as HS) but also works fine with semantic parsing.
